When houses wear faces: Reverse correlation applied to architectural design

Abstract

Reverse correlation (RC) is a data-driven method from social psychology that has been effectively shown to visualize the mental representations that humans hold regarding facial attributes. The method helps to understand what features are relevant in terms of the evaluation of faces, such as dominance or submissiveness. To the best of our knowledge, RC has solely been applied to faces within the area of psychology until this point. However, there are many other areas where it is of interest to understand how humans evaluate and visualize content, one of them being the evaluation of house facades. With this work, we extended the application of RC to architectural design, specifically focusing on the evaluation of house facades with respect to the psychological attributes of facelikeness, invitingness, and likeability. Furthermore, we propose a novel approach to create the base image, by utilizing a generative adversarial network. In an online study with a between-subject design, 121 participants completed the RC task, with 40 to 41 participants assigned to each of the three attributes. The resulting classification images (CIs) from the RC task unveil face-related features for the attribute facelikeness, signifying the potential extension of the RC methodology beyond the established domain of facial analysis to other domains, such as architectural design.